52,839 research outputs found
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
Active learning (AL) combines data labeling and model training to minimize
the labeling cost by prioritizing the selection of high value data that can
best improve model performance. In pool-based active learning, accessible
unlabeled data are not used for model training in most conventional methods.
Here, we propose to unify unlabeled sample selection and model training towards
minimizing labeling cost, and make two contributions towards that end. First,
we exploit both labeled and unlabeled data using semi-supervised learning (SSL)
to distill information from unlabeled data during the training stage. Second,
we propose a consistency-based sample selection metric that is coherent with
the training objective such that the selected samples are effective at
improving model performance. We conduct extensive experiments on image
classification tasks. The experimental results on CIFAR-10, CIFAR-100 and
ImageNet demonstrate the superior performance of our proposed method with
limited labeled data, compared to the existing methods and the alternative AL
and SSL combinations. Additionally, we study an important yet under-explored
problem -- "When can we start learning-based AL selection?". We propose a
measure that is empirically correlated with the AL target loss and is
potentially useful for determining the proper starting point of learning-based
AL methods.Comment: Accepted by ECCV202
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following knowledge, which we call the \emph{feature sign}---whether or not
a particular feature has on average stronger values over positive samples than
over negatives. We show how this can be estimated using as few as a single
labeled training sample per class. Then, using these feature signs, we extend
an initial supervised learning problem into an (almost) unsupervised clustering
formulation that can incorporate new data without requiring ground truth
labels. Our method works both as a feature selection mechanism and as a fully
competitive classifier. It has important properties, low computational cost and
excellent accuracy, especially in difficult cases of very limited training
data. We experiment on large-scale recognition in video and show superior speed
and performance to established feature selection approaches such as AdaBoost,
Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.Comment: arXiv admin note: text overlap with arXiv:1411.771
Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between runs on the same dataset. We propose perplexity based
graph construction and a new hierarchical subquery evaluation algorithm to
combat this variability, and to release the potential of Expected Error
Reduction.
Under some specific circumstances, Expected Error Reduction has been one of
the strongest-performing informativeness criteria for active learning. Until
now, it has also been prohibitively costly to compute for sizeable datasets. We
demonstrate our highly practical algorithm, comparing it to other active
learning measures on classification datasets that vary in sparsity,
dimensionality, and size. Our algorithm is consistent over multiple runs and
achieves high accuracy, while querying the human expert for labels at a
frequency that matches their desired time budget.Comment: CVPR 201
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